Random wobble area detection method based on probability density evaluation

Abstract

The invention relates to a random wobble area detection method based on probability density evaluation. In the existing method, a background model is difficult to be suitable for the interference of random wobble timely. According to the invention, the method comprises the following steps of: firstly, carrying out background modeling on a collected real-time video image, wherein a parameter-free kernel density evaluation method is adopted according to the invention; then extracting characteristics of a probability density distribution curve of a background pixel, calculating the evaluation coefficient of the background pixel, and determining the random wobble area by adopting a self-adaption method; and finally, evaluating the variation trend of the probability density distribution curve, and updating the threshold of the evaluation coefficient so as to be adaptive to the time sequence variation of random wobble. According to the invention, the random wobble area is detected by utilizing the characteristics of the probability density distribution curve of the background, so that better effects are obtained, and the accuracy of background model is improved; and the method only adopts the distribution characteristic of the background model, and redundant calculation amount is not increased, so that the timeliness of the method is ensured.

Description

technical field [0001] The invention belongs to the technical field of moving target detection in computer vision, and relates to a random swing region detection method based on probability density estimation. Background technique [0002] Background subtraction technology is a widely used motion detection technology, which is often used in traffic monitoring, security monitoring, human-computer interaction and other fields. The main methods are single Gaussian, codebook, mixed Gaussian, kernel density estimation, etc. However, in practical applications, due to various forms of illumination changes, random swings of branches and water waves, wind, rain, snow and fog, etc., how to establish a dynamic background model , extracting the background from the video containing moving objects is the key to the application of this technology. Among them, the detection and removal of random swings is one of the key issues in background modeling. At present, the main methods for detec...

AI Technical Summary

Problems solved by technology

At present, the main methods for detecting and removing random swings are: modeling the random swing area as part of the background model, such as mixed Gaussian, kernel density estimation and other common background modeling methods, but the background model of this type of method cannot be adapted in time Interference from random swings; extract apparent and spatiotemporal features, such as shape, dynamic texture, etc., from known random swing regions, and subtract background images. This type of method can remove swing disturbances with prior characteristics, while Strong interference cannot be removed

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